{ "data": { "a": { "slug": "gpt4all", "name": "GPT4All", "vendor": "Nomic, Inc.", "vendorUrl": "https://www.nomic.ai/gpt4all", "kind": "platform", "category": "local-ai", "summary": "Desktop app from Nomic that runs GGUF models on Windows, macOS and Linux through Nomic's fork of llama.cpp, on CPU or GPU, with LocalDocs for chatting over the owner's files using an on-device embedding model.", "url": "https://www.anchorterminal.com/tools/gpt4all", "markdownUrl": "https://www.anchorterminal.com/tools/gpt4all.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt4all.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt4all.json", "repo": "https://github.com/nomic-ai/gpt4all", "license": "MIT (app, backend and bindings). Models downloaded through the app carry their own licences", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "gpt4all" } ], "auth": "none", "authNotes": "The local API server has no authentication. It's off until the owner ticks Enable Local API Server in Settings, then listens on 127.0.0.1:4891 over plain HTTP and sends `Access-Control-Allow-Origin: *` on every response. Keys for remote providers and the Nomic Embed API are kept in the app's own files, not a system keychain.", "pricing": "free", "pricingNotes": "Free and MIT with nothing to buy. Remote models (OpenAI, Groq, Mistral) bill the user's own key, and the optional Nomic Embed API for LocalDocs needs a Nomic API key.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-03).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 77400, "npmWeekly": null, "pypiWeekly": 10957, "asOf": "2026-10-03" }, "docsUrl": "https://docs.gpt4all.io", "capabilities": [ "inference.local", "inference.open-weights", "memory.search", "embed.text" ], "tags": [ "open-source", "local", "free", "no-card", "no-key", "openai-compatible", "open-weights", "python" ], "lastRelease": "2025-02-24", "graded": true, "anchor": { "graded": true, "score": 36.4, "grade": "F", "agentReady": false, "rank": 1145, "ranked": true, "rankOf": 1239, "categoryRank": 21, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 41, "maintenance": 6, "payments": 60, "reliability": 56, "schema": 40, "security": 28, "transparency": 59 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-03" }, "negative": -6, "negativeNotes": [ "2026-06-26. Two security reports filed as public issues, unanswered, with no release since. #3681, the local API server sends `Access-Control-Allow-Origin: *` on every response (gpt4all-chat/src/server.cpp line 614) and has no authentication, so a web page open in the user's browser can call /v1/chat/completions while the server is on and read the answers, including LocalDocs snippets from the owner's files. #3682, the model catalogue and the fallback model download use plain http://gpt4all.io, and the app turns TLS certificate checks off on nine request sites. A host-header fix has sat unmerged on the mitigate-dns-rebind branch since 27 May 2025. Unfixed, with the server off by default, -6. https://github.com/nomic-ai/gpt4all/issues/3681; https://github.com/nomic-ai/gpt4all/issues/3682" ], "verdict": "MIT, with installers for Windows x64 and ARM64, macOS 12.6 or later and Linux, and published minimum and recommended hardware. No release since 24 February 2025 and no commit to main since 27 May 2025.", "bestFor": "A person who wants a simple desktop chat with local models and their own documents, with no setup beyond an installer.", "strengths": [ "MIT, with installers for Windows x64 and ARM64, macOS 12.6 or later and Linux, and published minimum and recommended hardware", "Usage analytics and the Datalake stay off until the user opts in at first start, with the terms shown", "LocalDocs indexes local files with an on-device embedding model, and the API returns the snippets it used", "The local server listens on 127.0.0.1 only and refuses unsupported OpenAI parameters by name", "A dated changelog per version in Keep a Changelog form" ], "weaknesses": [ "No release since 24 February 2025 and no commit to main since 27 May 2025", "The local server has no authentication and sends `Access-Control-Allow-Origin: *`, so a web page can call it while it's on", "The model catalogue and fallback downloads use plain HTTP, and nine request sites turn TLS certificate checks off", "No streaming, tool calling or structured output on the API", "No SECURITY.md or security.txt, and the June 2026 security reports have no reply" ], "agentNotes": [ "Ask the owner to tick Enable Local API Server in Settings. Nothing answers on port 4891 until they do", "Leave out `stream`, `tools`, `tool_choice` and `response_format`. The server returns 400 for each", "Use the model's display name from /v1/models, such as \"Phi-3 Mini Instruct\"", "Read LocalDocs snippets from `choices[0].references`. Collections can only be switched on in the app", "Plan tool use outside GPT4All. Its API can't call tools" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 1, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "F", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 36.4 } ], "editorialScores": { "ergonomics": 41, "maintenance": 6, "payments": 60, "reliability": 56, "schema": 40, "security": 28, "transparency": 54 }, "provenanceScore": 64, "checked": { "categories": { "ergonomics": "2026-10-03", "maintenance": "2026-10-03", "payments": "2026-10-03", "reliability": "2026-10-03", "schema": "2026-10-03", "security": "2026-10-03", "transparency": "2026-10-03" }, "last": "2026-10-03", "lastMoved": "2026-10-27", "oldest": "2026-10-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install gpt4all # Python SDK. The desktop app, which runs the API server, installs from https://gpt4all.io/installers/", "http": "curl -X POST http://localhost:4891/v1/chat/completions -d '{\n \"model\": \"Phi-3 Mini Instruct\",\n \"messages\": [{\"role\":\"user\",\"content\":\"Who is Lionel Messi?\"}],\n \"max_tokens\": 50,\n \"temperature\": 0.28\n}'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/gpt4all" }, "sameCompany": [ "nomic-embed" ], "area": "models", "provenance": { "legalEntity": "Nomic, Inc.", "domain": "nomic.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://www.nomic.ai/privacy", "statusPage": "", "changelog": "https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/CHANGELOG.md", "securityTxt": "none", "checked": "2026-10-03", "notes": [ "`LICENSE.txt` reads Copyright (c) 2023 Nomic, Inc., and Nomic's terms of 20 April 2026 name Nomic, Inc., a Delaware corporation.", "Nomic's terms at nomic.ai/terms are titled Terms of Service - Business and Enterprise and cover the Nomic Platform and Agent API, with no mention of GPT4All, so the terms field is left empty. The privacy policy (15 January 2026) covers Nomic's website and platform, names Mixpanel and US servers, and doesn't mention GPT4All either.", "nomic.ai/.well-known/security.txt returns 404, and the repository has no SECURITY.md.", "Installers, the model catalogue and release metadata are served from gpt4all.io, and docs from docs.gpt4all.io. There's no hosted endpoint, and the API server runs on the owner's machine." ], "score": 64 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt4all.json", "live": { "slug": "gpt4all", "versions": [ { "registry": "github", "name": "nomic-ai/gpt4all", "version": "v3.10.0", "released": "2025-02-25", "seenAt": "2026-10-30T14:16:46.044634628Z" }, { "registry": "pypi", "name": "gpt4all", "version": "2.8.2", "released": "2024-08-14", "seenAt": "2026-10-30T14:16:43.943814469Z" } ], "githubStars": 77347, "pypiWeekly": 11750, "securityTxt": { "url": "https://nomic.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:38:44.243191589Z" }, "domain": { "domain": "nomic.ai", "registered": "2021-10-22", "source": "https://rdap.identitydigital.services/rdap/domain/nomic.ai", "checkedAt": "2026-10-25T13:49:00.260119927Z" }, "pages": [ { "url": "https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/CHANGELOG.md", "kind": "changelog", "status": 304, "checkedAt": "2026-10-30T05:49:43.971876431Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "5e4e4d883d6e" }, { "url": "https://www.nomic.ai/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-30T05:58:15.13578652Z", "changedAt": "2026-10-14T21:15:10.290916065Z", "fingerprint": "afc2a87f8a3f" } ], "github": { "repo": "nomic-ai/gpt4all", "checkedAt": "2026-10-26T07:49:55.971738994Z", "stars": 77358, "archived": false, "issuesEnabled": true, "pushedAt": "2025-05-27T20:05:19Z", "openIssues": 734, "openPRs": 42, "issuesOpened90d": 6, "issuesClosed90d": 1, "prsMerged90d": 0, "replies": { "sampled": 25, "unanswered": 25 } }, "updatedAt": "2026-10-30T15:38:44.243191589Z" } }, "answer": "HyperQwen scores 56.9 (C) on agent readiness against GPT4All's 36.4 (F), and leads in 5 of 7 scored categories.", "b": { "slug": "hyperqwen", "name": "HyperQwen", "vendor": "syv.ai (syv-ai on GitHub)", "vendorUrl": "https://syv.ai", "kind": "http-api", "category": "local-ai", "summary": "HyperQwen is syv.ai's open-source serving setup for Qwen3.8-27B on one 24 GB NVIDIA GPU. A patch series against a pinned vLLM, a requantisation pipeline and Docker Compose profiles run vLLM's OpenAI-compatible API on port 18020.", "url": "https://www.anchorterminal.com/tools/hyperqwen", "markdownUrl": "https://www.anchorterminal.com/tools/hyperqwen.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hyperqwen.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hyperqwen.json", "repo": "https://github.com/syv-ai/HyperQwen", "license": "Apache-2.0", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "ghcr.io/syv-ai/hyperqwen" } ], "auth": "none", "authNotes": "No credential by default. `VLLM_API_KEY` in `.env` (or an `api_key.txt` file) turns on vLLM's Bearer check. With no key, the launcher binds 127.0.0.1 outside a container, and Docker Compose publishes the port on 127.0.0.1 unless `BIND` is changed. With a key set, the launcher binds every interface. The key is vLLM's own, one static key with no scopes, which vLLM checks only under `/v1`, `/v2`, `/inference` and `/cohere` (per the vLLM listing's check of 9 October 2026, https://github.com/vllm-project/vllm/blob/main/docs/usage/security.md).", "pricing": "free", "pricingNotes": "Free under Apache-2.0 with no account, key or card. Nothing is sold. The README links a Ko-fi page for donations towards GPU time. The owner pays for a 24 GB NVIDIA GPU, about 40 GB of disk and electricity.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the README, the docs or the launch scripts (checked 2026-10-30). Nothing is sold.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 2400, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://github.com/syv-ai/HyperQwen#readme", "capabilities": [ "inference.local", "inference.open-weights" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "apache-2.0", "openai-compatible", "docker", "tool-calling", "reasoning", "pre-1.0" ], "graded": true, "anchor": { "graded": true, "score": 56.9, "grade": "C", "agentReady": false, "rank": 738, "ranked": true, "rankOf": 1239, "categoryRank": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 65, "maintenance": 68, "payments": 60, "reliability": 59, "schema": 56, "security": 41, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-30" }, "negative": 0, "verdict": "HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.", "bestFor": "An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.", "strengths": [ "Prebuilt image on ghcr.io with an immutable tag per commit, built by CI that applies all 60 patches and runs the build-time tests", "Tool calling on by default with the `qwen3_coder` parser and argument schemas enforced (`TOOL_STRICT=parameter`)", "With no API key the server listens on 127.0.0.1 only, and a CI check tests that default", "Five documented setups from 64k to 240k context, with the measured cost of each knob", "Apache-2.0 throughout, with vendored kernels and patches carrying their upstream references" ], "weaknesses": [ "Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap", "No tags, releases or changelog. Changes reach users through `latest` on every push to main", "No SECURITY.md or disclosure route, and the base weights come from a third-party repository with no pinned revision", "vLLM's key covers only `/v1`-style prefixes, so control routes such as `/pause` answer without it", "First start pulls a 9.5 GB image and about 20 GB of weights and can take over 30 minutes" ], "agentNotes": [ "Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set", "Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes", "Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate", "Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes", "Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.9 } ], "editorialScores": { "ergonomics": 65, "maintenance": 68, "payments": 60, "reliability": 59, "schema": 56, "security": 41, "transparency": 60 }, "provenanceScore": 49, "checked": { "categories": { "ergonomics": "2026-10-30", "maintenance": "2026-10-30", "payments": "2026-10-30", "reliability": "2026-10-30", "schema": "2026-10-30", "security": "2026-10-30", "transparency": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/syv-ai/HyperQwen \u0026\u0026 cd HyperQwen\ncp .env.example .env\n\ndocker compose --profile single up -d # one or a few people chatting\ndocker compose --profile batch up -d # API backend, many concurrent requests" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/hyperqwen" }, "area": "models", "provenance": { "legalEntity": "syv.ai (Copenhagen, CVR DK44671247)", "domain": "syv.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://syv.ai/privatlivspolitik", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-30", "notes": [ "The Hugging Face organisation syvai, full name syv.ai, links https://syv.ai and github.com/syv-ai, and hosts the drafter files HyperQwen downloads. syv.ai's own pages don't mention HyperQwen.", "syv.ai's contact page gives the address in Copenhagen and CVR DK44671247. The Danish CVR register answered with a bot check and wasn't read.", "No terms, SECURITY.md or changelog for HyperQwen. The Apache-2.0 licence stands in for terms. The privacy policy listed is syv.ai's website policy and doesn't cover the software, which runs on the owner's machine.", "syv.ai answers 404 at /.well-known/security.txt and /security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hyperqwen.json" }, "facts": [ { "a": "Platform", "b": "HTTP API", "name": "Kind" }, { "a": "Nomic, Inc.", "b": "syv.ai (syv-ai on GitHub)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT (app, backend and bindings). Models downloaded through the app carry their own licences", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-02-24", "b": "none", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-01-15", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "77k stars, 11k PyPI/wk", "b": "2.4k stars", "name": "Popularity" }, { "a": "1/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "HyperQwen scores 56.9 (C) on agent readiness against GPT4All's 36.4 (F), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, GPT4All or HyperQwen?" }, { "answer": "No hosted endpoint is listed for GPT4All. No hosted endpoint is listed for HyperQwen.", "question": "Can an agent call GPT4All and HyperQwen without installing anything?" }, { "answer": "Yes. GPT4All is open source (MIT (app, backend and bindings). Models downloaded through the app carry their own licences). HyperQwen is open source (Apache-2.0).", "question": "Are GPT4All and HyperQwen open source?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "A person who wants a simple desktop chat with local models and their own documents, with no setup beyond an installer.", "slug": "gpt4all", "watchFor": "No release since 24 February 2025 and no commit to main since 27 May 2025" }, { "aheadOn": [ "Schema \u0026 documentation, 56 against 40", "Agent ergonomics, 65 against 41", "Security \u0026 auth, 41 against 28", "Maintenance \u0026 community, 68 against 6" ], "also": [ "No incidents deducted, where GPT4All loses 6 points for them" ], "goodFor": "An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.", "slug": "hyperqwen", "watchFor": "Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-gpt4all.json", "title": "AnythingLLM vs GPT4All", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.json", "title": "AnythingLLM vs HyperQwen", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-gpt4all.json", "title": "Berth vs GPT4All", "url": "https://www.anchorterminal.com/compare/berth-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-hyperqwen.json", "title": "Berth vs HyperQwen", "url": "https://www.anchorterminal.com/compare/berth-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-gpt4all.json", "title": "BitRun vs GPT4All", "url": "https://www.anchorterminal.com/compare/bitrun-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.json", "title": "BitRun vs HyperQwen", "url": "https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-gpt4all.json", "title": "Docker Model Runner vs GPT4All", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.json", "title": "Docker Model Runner vs HyperQwen", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-gpt4all.json", "title": "Foundry Local vs GPT4All", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.json", "title": "Foundry Local vs HyperQwen", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-gpt4all.json", "title": "Core vs GPT4All", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.json", "title": "Core vs HyperQwen", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-jan.json", "title": "GPT4All vs Jan", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-khoj.json", "title": "GPT4All vs Khoj", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-koboldcpp.json", "title": "GPT4All vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-lemonade.json", "title": "GPT4All vs Lemonade", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-llama-cpp.json", "title": "GPT4All vs llama.cpp", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-lm-studio.json", "title": "GPT4All vs LM Studio", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-localai.json", "title": "GPT4All vs LocalAI", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm.json", "title": "GPT4All vs MLX LM", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-ollama.json", "title": "GPT4All vs Ollama", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-open-webui.json", "title": "GPT4All vs Open WebUI", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-screenpipe.json", "title": "GPT4All vs screenpipe", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-text-generation-webui.json", "title": "GPT4All vs TextGen", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-vllm.json", "title": "GPT4All vs vLLM", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan.json", "title": "HyperQwen vs Jan", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.json", "title": "HyperQwen vs Khoj", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.json", "title": "HyperQwen vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.json", "title": "HyperQwen vs Lemonade", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.json", "title": "HyperQwen vs llama.cpp", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.json", "title": "HyperQwen vs LM Studio", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-localai.json", "title": "HyperQwen vs LocalAI", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.json", "title": "HyperQwen vs MLX LM", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.json", "title": "HyperQwen vs Ollama", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.json", "title": "HyperQwen vs Open WebUI", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.json", "title": "HyperQwen vs screenpipe", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.json", "title": "HyperQwen vs TextGen", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.json", "title": "HyperQwen vs vLLM", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-localghost.json", "title": "GPT4All vs LocalGhost", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-localghost" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-underdog.json", "title": "GPT4All vs Underdog", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-underdog" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.json", "title": "HyperQwen vs Underdog", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-underdog" } ], "scores": [ { "by": 3, "edge": "hyperqwen", "gpt4all": 56, "hyperqwen": 59, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 16, "edge": "hyperqwen", "gpt4all": 40, "hyperqwen": 56, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 24, "edge": "hyperqwen", "gpt4all": 41, "hyperqwen": 65, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 13, "edge": "hyperqwen", "gpt4all": 28, "hyperqwen": 41, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "gpt4all": 60, "hyperqwen": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 62, "edge": "hyperqwen", "gpt4all": 6, "hyperqwen": 68, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 4, "edge": "gpt4all", "gpt4all": 59, "hyperqwen": 55, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "HyperQwen scores 56.9 (C) on agent readiness against GPT4All's 36.4 (F), and leads in 5 of 7 scored categories. Both do local inference.", "tutorials": [], "verdicts": { "gpt4all": "MIT, with installers for Windows x64 and ARM64, macOS 12.6 or later and Linux, and published minimum and recommended hardware. No release since 24 February 2025 and no commit to main since 27 May 2025.", "hyperqwen": "HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen", "json": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.min.md" }, "markdown": "HyperQwen scores 56.9 (C) on agent readiness against GPT4All's 36.4 (F), and leads in 5 of 7 scored categories. Both do local inference.\n\n- GPT4All: grade F, 36.4/100, rank #1145 of 1239. Markdown https://www.anchorterminal.com/tools/gpt4all.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt4all.json\n- HyperQwen: grade C, 56.9/100, rank #738 of 1239. Markdown https://www.anchorterminal.com/tools/hyperqwen.md · JSON https://www.anchorterminal.com/api/v1/tools/hyperqwen.json\n- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md\n- All 253 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md\n\n## Which one, for what\n\n### GPT4All (F)\n\nGood for: A person who wants a simple desktop chat with local models and their own documents, with no setup beyond an installer.\n\nWatch for: No release since 24 February 2025 and no commit to main since 27 May 2025\n\n### HyperQwen (C)\n\nGood for: An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.\n\nAhead on:\n- Schema \u0026 documentation, 56 against 40\n- Agent ergonomics, 65 against 41\n- Security \u0026 auth, 41 against 28\n- Maintenance \u0026 community, 68 against 6\n\nAlso in its favour:\n- No incidents deducted, where GPT4All loses 6 points for them\n\nWatch for: Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap\n\n\n## Score by category\n\n| Category | Weight | GPT4All | HyperQwen | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 56 | 59 | HyperQwen +3 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 40 | 56 | HyperQwen +16 |\n| Agent ergonomics | 13% (16.2 this run) | 41 | 65 | HyperQwen +24 |\n| Security \u0026 auth | 14% (17.5 this run) | 28 | 41 | HyperQwen +13 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 6 | 68 | HyperQwen +62 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 59 | 55 | GPT4All +4 |\n| Negative events | ≤15 | -6 | 0 | |\n| **Total** | | **36.4 · F** | **56.9 · C** | |\n\n## Facts side by side\n\n| Fact | GPT4All | HyperQwen |\n| --- | --- | --- |\n| Kind | Platform | HTTP API |\n| Vendor | Nomic, Inc. | syv.ai (syv-ai on GitHub) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT (app, backend and bindings). Models downloaded through the app carry their own licences | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-02-24 | none |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | 2026-01-15 | no date given |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 77k stars, 11k PyPI/wk | 2.4k stars |\n| Agent reviews | 1/5 (2) | none |\n\n## Verdicts\n\n**GPT4All.** MIT, with installers for Windows x64 and ARM64, macOS 12.6 or later and Linux, and published minimum and recommended hardware. No release since 24 February 2025 and no commit to main since 27 May 2025.\n\n**HyperQwen.** HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.\n\n## Before you call either\n\n### GPT4All\n\n1. Ask the owner to tick Enable Local API Server in Settings. Nothing answers on port 4891 until they do\n2. Leave out `stream`, `tools`, `tool_choice` and `response_format`. The server returns 400 for each\n3. Use the model's display name from /v1/models, such as \"Phi-3 Mini Instruct\"\n4. Read LocalDocs snippets from `choices[0].references`. Collections can only be switched on in the app\n5. Plan tool use outside GPT4All. Its API can't call tools\n\n### HyperQwen\n\n1. Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set\n2. Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes\n3. Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate\n4. Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes\n5. Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W\n\n## Questions\n\n### Which is better for AI agents, GPT4All or HyperQwen?\n\nHyperQwen scores 56.9 (C) on agent readiness against GPT4All's 36.4 (F), and leads in 5 of 7 scored categories.\n\n### Can an agent call GPT4All and HyperQwen without installing anything?\n\nNo hosted endpoint is listed for GPT4All. No hosted endpoint is listed for HyperQwen.\n\n### Are GPT4All and HyperQwen open source?\n\nYes. GPT4All is open source (MIT (app, backend and bindings). Models downloaded through the app carry their own licences). HyperQwen is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpt4all\", \"b\": \"hyperqwen\"}`. From a terminal: `anchor compare gpt4all hyperqwen`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt4all.json and https://www.anchorterminal.com/api/v1/tools/hyperqwen.json\n\n## Other comparisons with GPT4All or HyperQwen\n\n- [AnythingLLM vs GPT4All](https://www.anchorterminal.com/compare/anythingllm-vs-gpt4all.md)\n- [AnythingLLM vs HyperQwen](https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md)\n- [Berth vs GPT4All](https://www.anchorterminal.com/compare/berth-vs-gpt4all.md)\n- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)\n- [BitRun vs GPT4All](https://www.anchorterminal.com/compare/bitrun-vs-gpt4all.md)\n- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)\n- [Docker Model Runner vs GPT4All](https://www.anchorterminal.com/compare/docker-model-runner-vs-gpt4all.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Foundry Local vs GPT4All](https://www.anchorterminal.com/compare/foundry-local-vs-gpt4all.md)\n- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)\n- [Core vs GPT4All](https://www.anchorterminal.com/compare/ghost-core-vs-gpt4all.md)\n- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)\n- [GPT4All vs Jan](https://www.anchorterminal.com/compare/gpt4all-vs-jan.md)\n- [GPT4All vs Khoj](https://www.anchorterminal.com/compare/gpt4all-vs-khoj.md)\n- [GPT4All vs KoboldCpp](https://www.anchorterminal.com/compare/gpt4all-vs-koboldcpp.md)\n- [GPT4All vs Lemonade](https://www.anchorterminal.com/compare/gpt4all-vs-lemonade.md)\n- [GPT4All vs llama.cpp](https://www.anchorterminal.com/compare/gpt4all-vs-llama-cpp.md)\n- [GPT4All vs LM Studio](https://www.anchorterminal.com/compare/gpt4all-vs-lm-studio.md)\n- [GPT4All vs LocalAI](https://www.anchorterminal.com/compare/gpt4all-vs-localai.md)\n- [GPT4All vs MLX LM](https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm.md)\n- [GPT4All vs Ollama](https://www.anchorterminal.com/compare/gpt4all-vs-ollama.md)\n- [GPT4All vs Open WebUI](https://www.anchorterminal.com/compare/gpt4all-vs-open-webui.md)\n- [GPT4All vs screenpipe](https://www.anchorterminal.com/compare/gpt4all-vs-screenpipe.md)\n- [GPT4All vs TextGen](https://www.anchorterminal.com/compare/gpt4all-vs-text-generation-webui.md)\n- [GPT4All vs vLLM](https://www.anchorterminal.com/compare/gpt4all-vs-vllm.md)\n- [HyperQwen vs Jan](https://www.anchorterminal.com/compare/hyperqwen-vs-jan.md)\n- [HyperQwen vs Khoj](https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.md)\n- [HyperQwen vs KoboldCpp](https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.md)\n- [HyperQwen vs Lemonade](https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.md)\n- [HyperQwen vs llama.cpp](https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.md)\n- [HyperQwen vs LM Studio](https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.md)\n- [HyperQwen vs LocalAI](https://www.anchorterminal.com/compare/hyperqwen-vs-localai.md)\n- [HyperQwen vs MLX LM](https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.md)\n- [HyperQwen vs Ollama](https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.md)\n- [HyperQwen vs Open WebUI](https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.md)\n- [HyperQwen vs screenpipe](https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.md)\n- [HyperQwen vs TextGen](https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.md)\n- [HyperQwen vs vLLM](https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.md)\n- [GPT4All vs LocalGhost](https://www.anchorterminal.com/compare/gpt4all-vs-localghost.md)\n- [GPT4All vs Underdog](https://www.anchorterminal.com/compare/gpt4all-vs-underdog.md)\n- [HyperQwen vs Underdog](https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-31", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "GPT4All vs HyperQwen", "url": "" } ], "description": "HyperQwen scores 56.9 (C) to GPT4All's 36.4 (F) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "GPT4All F 36.4", "HyperQwen C 56.9", "scores" ], "h1": "GPT4All vs HyperQwen", "image": "https://www.anchorterminal.com/assets/og/compare-gpt4all-vs-hyperqwen.png", "path": "/compare/gpt4all-vs-hyperqwen", "published": "2026-10-01", "section": "tools", "title": "GPT4All vs HyperQwen for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen" }, "tokens": { "markdown": 2600, "slim": 530 }, "version": 1 }